Abstract Machine Learning (ML) has been enjoying an unprecedented surge in applications
that solve problems and enable automation in diverse domains. Primarily, this is due to the …

Reinforcement learning (RL), which is a class of machine learning, provides a framework by
which a system can learn from its previous interactions with its environment to efficiently …

The introduction of intelligent devices with short range wireless communication techniques
has motivated the development of Mobile Ad hoc NETworks (MANETs) during the last few …

The dynamicity of distributed wireless networks caused by node mobility, dynamic network
topology, and others has been a major challenge to routing in such networks. In the …

With the increasing complexity of network topologies and architectures, adding intelligence
to the network control plane through Artificial Intelligence and Machine Learning (AI&ML) is …

The future network is expected to support extremely large bandwidth, ultra-low latency or
deterministic delay, extremely high reliability, and massive connectivity for novel forward …

The utilization of UAVs in various fields has led to the development of flying ad hoc network
(FANET) technology. In a network environment with highly dynamic topology and frequent …

Wireless sensor nodes are battery-powered devices which makes the design of energy-
efficient Wireless Sensor Networks (WSNs) a very challenging issue. In this paper, we …

This paper proposes a novel routing protocol for opportunistic networks called Fuzzy logic-
based Q-Learning Routing Protocol (FQLRP), which uses fuzzy based Qlearning for efficient …

Abstract Delay Tolerant Reinforcement-Based (DTRB) is a delay tolerant routing solution for
IEEE 802.11 wireless networks which enables device to device data exchange without the …